Wednesday, August 17, 2016

NVDA has a $2 billion R&D head start but this is worth keeping an eye on.
In addition, with the stock down two days in a row and $1.32 below the most recent all-time high this is probably a good time to point out the drawdowns can be big in this type of investing. That said, NVDA is the class of the field. $61.98 down 62 cents.

From IEEE Spectrum:

Deep-learning artificial intelligence has mostly relied upon the
general-purpose GPU hardware used in many other computing tasks. But
Intel’s recent acquisition of the startup Nervana Systems will give the
tech giant ownership of a specialized chip designed specifically for
deep learning AI applications. That could give Intel a huge lead in the
race to develop next-generation artificial intelligence capable of
swiftly finding patterns in huge data sets and learning through
imitation.

Nervana has leaned heavily on GPU hardware to build its own portfolio
of deep-learning AI services for both companies and independent
developers. But the startup has also been developing its own specialized
deep-learning hardware, called Nervana Engine,
that includes only the components necessary for running deep-learning
algorithms and eliminates the extra components used for general-purpose
GPU tasks. Nervana claims that when the Engine chip comes
out in 2017, it will deliver around 10 times as much computing power
for deep learning as the best of today’s GPUs.

“Nervana’s AI expertise combined with Intel’s capabilities and huge
market reach will allow us to realize our vision and create something
truly special,” said Naveen Rao, CEO and cofounder of Nervana, in a blog post.

Software algorithms known as artificial neural networks are the heart of deep-learning AI. Such
algorithms learn how to perform certain tasks through imitation and by
observing correctly labeled examples as they sift through huge amounts
of data.To accommodate deep learning’s voracious appetite
for data, Nervana’s Engine hardware design includes High Bandwidth
Memory technology that has stacked memory and densely packed data
channels to swiftly move around large amounts of data.

The end result: 32 gigabytes of on-chip storage and up to 8 terabits per second of memory access speed. By comparison, the GDDR5 memory technology used in GPUs has memory access speeds of just 224 gigabits per second....MORE